Improving maritime accident severity prediction accuracy: A holistic machine learning framework with data balancing and explainability techniques

作者：Wenjie Cao, Xinjian Wang, Yuanjun Feng, Jingen Zhou, Zaili Yang · 发表于：Reliability Engineering & System Safety · 年份：2025 · DOI：10.1016/j.ress.2025.111648 · 被引用次数：36 · 研究领域：Maritime Navigation and Safety、Anomaly Detection Techniques and Applications、Risk and Safety Analysis

• A holistic machine learning framework is developed to conduct maritime accident severity prediction. • The framework integrates eight well-established models, employing rigorous hyperparameter tuning and cross-validation. • Six advanced data balancing techniques are employed to mitigate class imbalance. • A novel dual interpretability approach combining SHAP and LIME provides both global and local insights. • The proposed framework offering a robust basis for risk-informed maritime safety management. Accurately predicting the severity of maritime accidents is crucial for enhancing safety management and minimizing operational risks. Traditional prediction models, however, often suffer from the challenges resulted from unbalanced datasets and the complexity of multidimensional factors. This study aims to develop an integrated prediction framework incorporating six data balancing techniques to effectively address category imbalance and enhance model predictive robustness. Additionally, eight well-established machine learning models are utilized, with their performance optimized through hyperparameter tuning and cross-validation. To interpret the model results, SHapley Additive exPlanations (SHAP) are applied for global feature contribution analysis, while Local Interpretable Model-agnostic Explanations (LIME) provide local interpretations, enabling an in-depth understanding of feature-specific impacts on predictions. The results indicate that the combination of RandomOverSampl...